Abstract
Computational Grids are a promising platform for executing large-scale resource
intensive applications. However, resource management and scheduling in the Grid
environment is a complex undertaking as resources are (geographically)
distributed, heterogeneous in nature, owned by different individuals or
organizations with their own policies, have different access and cost models,
and have dynamically varying loads and availability. This introduces a number
of challenging issues such as site autonomy, heterogeneous interaction, policy
extensibility, resource allocation or co-allocation, online control,
scalability, transparency, resource brokering, and computational economy.
A number of Grid systems (such as Globus and Legion) have addressed many of
these issues with exception of a computational economy. We argue that a
computational economy is required in order to create a real world scalable Grid
because it provides a mechanism for regulating the Grid resources demand and
supply. It offers incentive for resource owners to be part of the Grid and
encourages consumers to optimally utilize resources and balance timeframe and
access costs. We propose a computational economy framework that builds on the
existing Grid middleware systems and offers an infrastructure for resource
management and trading in the Grid environment. We discuss the usage economic
models for resource trading in the Nimrod/G resource broker and present
deadline and cost-based scheduling experimental results on the Grid.